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L1592_Frame_C02  Page 8  Tuesday, December 18, 2001  1:40 PM







                       Example 2.1

                           A laboratory’s measurement process was assessed by randomly inserting 27 specimens having
                           a known concentration of η = 8.0 mg/L into the normal flow of work over a period of 2 weeks.
                           A large number of measurements were being done routinely and any of several chemists might
                           be assigned any sample specimen. The chemists were ‘blind’ to the fact that performance was
                           being assessed. The ‘blind specimens’ were outwardly identical to all other specimens passing
                           through the laboratory. This arrangement means that observed values are random and independent.
                           The results in order of observation were 6.9, 7.8, 8.9, 5.2, 7.7, 9.6, 8.7, 6.7, 4.8, 8.0, 10.1, 8.5,
                           6.5, 9.2, 7.4, 6.3, 5.6, 7.3, 8.3, 7.2, 7.5, 6.1, 9.4, 5.4, 7.6, 8.1, and 7.9 mg/L.
                             The population is all specimens having a known concentration of 8.0 mg/L. The sample is
                           the 27 observations (measurements). The  sample size is  n  = 27. The  random variable is the
                           measured concentration in each specimen having a known concentration of 8.0 mg/L. Experi-
                           mental error has caused the observed values to vary about the true value of 8.0 mg/L. The errors
                           are 6.9 − 8.0 = −1.1, 7.8 − 8.0 = −0.2, +0.9, −2.8, −0.3, +1.6, +0.7, and so on.



                       Plotting the Data

                       A useful first step is to plot the data. Figure 2.1 shows the data from Example 2.1 plotted in time order
                       of observation, with a dot diagram plotted on the right-hand side. Dots are stacked to indicate frequency.
                        A dot diagram starts to get crowded when there are more than about 20 observations. For a large
                       number of points (a large sample size), it is convenient to group the dots into intervals and represent a
                       group with a bar, as shown in Figure 2.2. This plot shows the empirical (realized) distribution of the
                       data. Plots of this kind are usually called histograms, but the more suggestive name of data density plot
                       has been suggested (Watts, 1991).


                                               12
                                             Nitrate     (mg/L)  8           • •••

                                                                             ••••••
                                                                             ••••••••
                                                                             •••••
                                                                             •
                                               4
                                                 0       10       20       30 •••
                                                       Order of Observation

                       FIGURE 2.1 Time plot and dot diagram (right-hand side) of the nitrate data in Example 2.1.

                                                  10
                                                   8
                                                 Frequency  6 4




                                                   2
                                                   0
                                                     4      5      6     7      8      9    10
                                                           Nitrate (mg/L)


                       FIGURE 2.2 Frequency diagram (histogram).
                       © 2002 By CRC Press LLC
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